AbstractClassifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed categories, they fail to handle novel instances of that are frequently presented the learning algorithm. To this end, trivial solution train model with classes. However, may result phenomen...

Skin cancer is the most annoying type of diagnosis according to its fast spread various body areas, so it was necessary establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks (CNNs) are used classify images skin cancer. This paper tries get accurate model and detect types from seven different classes using deep learning techni...

Unclassified region decreases the efficiency and performance of multi-class support vector machine. The proper selection of feature sub set reduced the unclassified region of multi-class support vector machine. Now a day’s multi-class classification are widely used in image classification. The feature selection or mapping of data one space to another space creates diversity of outlier and noise...

Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia as a time series classification problem. Meanwhile, early presents real-world demand for diagnosis. In this paper, we address problem diseases classification, which varied-length long-length well. For solving problem, propose deep...

The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multiclass classification problems into binary classification problems is specified using a recursive c...

Most of the text classification problems are associated with multiple class labels and hence automatic text classification is one of the most challenging and prominent research area. Text classification is the problem of categorizing text documents into different classes. In the multi-label classification scenario, each document is associated may have more than one label. The real challenge in ...